Dissecting the expression patterns of transcription factors across conditions using an integrated network-based approach

In prokaryotes, regulation of gene expression is predominantly controlled at the level of transcription. Transcription in turn is mediated by a set of DNA-binding factors called Transcription Factors (TFs). In this study, we map the complete repertoire of ~ 300 TFs of the bacterial model, Escherichi...

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Detalles Bibliográficos
Autores: Janga, Sarath Chandra, Contreras-Moreira, Bruno
Tipo de recurso: artículo
Fecha de publicación:2010
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/27237
Acceso en línea:http://hdl.handle.net/10261/27237
Access Level:acceso abierto
Palabra clave:Gene regulation
Expression
Transcription factors
Network dynamics
Escherichia coli
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spelling Dissecting the expression patterns of transcription factors across conditions using an integrated network-based approachJanga, Sarath ChandraContreras-Moreira, BrunoGene regulationExpressionTranscription factorsNetwork dynamicsEscherichia coliIn prokaryotes, regulation of gene expression is predominantly controlled at the level of transcription. Transcription in turn is mediated by a set of DNA-binding factors called Transcription Factors (TFs). In this study, we map the complete repertoire of ~ 300 TFs of the bacterial model, Escherichia coli, onto gene expression data for a number of non-redundant experimental conditions and show that TFs are generally expressed at a lower level than other gene classes. We also demonstrate that different conditions harbor varying number of active TFs, with an average of about 15% of the total repertoire, with certain stress and drug induced conditions exhibiting as high as one-third of the collection of TFs. Our results also show that activators are more frequently expressed than repressors, indicating that activation of promoters might be a more common phenomenon than repression in bacteria. Finally, to understand the association of TFs with different conditions and to elucidate their dynamic interplay with other TFs, we develop a network-based framework to identify TFs which act as markers, those which are responsible for condition-specific transcriptional rewiring. This approach allowed us to pinpoint several marker TFs as being central in various specialized conditions like drug-induction or growth condition variations, which we discuss in light of previously reported experimental findings. Further analysis showed that a majority of identified markers effectively control the expression of their regulons and in general transcriptional programs of most conditions can be effectively rewired by a very small number of TFs. It was also found that closeness is a key centrality measure which can aid in the successful identification of marker TFs in regulatory networks. Our results suggest the utility of the network-based approaches developed in this study to be applicable for understanding other interactomic datasets.This work was supported by MRC Laboratory of Molecular Biology and Cambridge Commonwealth Trust and by a grant from Gobierno de Aragón to the research group of José María Lasa in 2010Peer reviewed201020102010info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_65013021299 bytesapplication/pdfhttp://hdl.handle.net/10261/27237reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.1093/nar/gkq612info:eu-repo/semantics/openAccessoai:digital.csic.es:10261/272372026-05-22T06:33:51Z
dc.title.none.fl_str_mv Dissecting the expression patterns of transcription factors across conditions using an integrated network-based approach
title Dissecting the expression patterns of transcription factors across conditions using an integrated network-based approach
spellingShingle Dissecting the expression patterns of transcription factors across conditions using an integrated network-based approach
Janga, Sarath Chandra
Gene regulation
Expression
Transcription factors
Network dynamics
Escherichia coli
title_short Dissecting the expression patterns of transcription factors across conditions using an integrated network-based approach
title_full Dissecting the expression patterns of transcription factors across conditions using an integrated network-based approach
title_fullStr Dissecting the expression patterns of transcription factors across conditions using an integrated network-based approach
title_full_unstemmed Dissecting the expression patterns of transcription factors across conditions using an integrated network-based approach
title_sort Dissecting the expression patterns of transcription factors across conditions using an integrated network-based approach
dc.creator.none.fl_str_mv Janga, Sarath Chandra
Contreras-Moreira, Bruno
author Janga, Sarath Chandra
author_facet Janga, Sarath Chandra
Contreras-Moreira, Bruno
author_role author
author2 Contreras-Moreira, Bruno
author2_role author
dc.subject.none.fl_str_mv Gene regulation
Expression
Transcription factors
Network dynamics
Escherichia coli
topic Gene regulation
Expression
Transcription factors
Network dynamics
Escherichia coli
description In prokaryotes, regulation of gene expression is predominantly controlled at the level of transcription. Transcription in turn is mediated by a set of DNA-binding factors called Transcription Factors (TFs). In this study, we map the complete repertoire of ~ 300 TFs of the bacterial model, Escherichia coli, onto gene expression data for a number of non-redundant experimental conditions and show that TFs are generally expressed at a lower level than other gene classes. We also demonstrate that different conditions harbor varying number of active TFs, with an average of about 15% of the total repertoire, with certain stress and drug induced conditions exhibiting as high as one-third of the collection of TFs. Our results also show that activators are more frequently expressed than repressors, indicating that activation of promoters might be a more common phenomenon than repression in bacteria. Finally, to understand the association of TFs with different conditions and to elucidate their dynamic interplay with other TFs, we develop a network-based framework to identify TFs which act as markers, those which are responsible for condition-specific transcriptional rewiring. This approach allowed us to pinpoint several marker TFs as being central in various specialized conditions like drug-induction or growth condition variations, which we discuss in light of previously reported experimental findings. Further analysis showed that a majority of identified markers effectively control the expression of their regulons and in general transcriptional programs of most conditions can be effectively rewired by a very small number of TFs. It was also found that closeness is a key centrality measure which can aid in the successful identification of marker TFs in regulatory networks. Our results suggest the utility of the network-based approaches developed in this study to be applicable for understanding other interactomic datasets.
publishDate 2010
dc.date.none.fl_str_mv 2010
2010
2010
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url http://hdl.handle.net/10261/27237
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